Processing of GAC NDVI data for yield forecasting in the Sahelian region

Abstract

Recent investigations have demonstrated that inter-year NOAAAVHRR NDVI variations in the middle of the rainy season can provide information on final crop yield in Sahelian countries. The present work continues this line of research by the use of 10-day Global Area Coverage (GAC) NDVI Maximum Value Composites, which are widely available and cost-effective in Africa. This use actually posed some problems which were mitigated by a multistep methodology aimed at forecasting millet and sorghum yield in Niger. The soil effect was first minimized in the NDVI images, and a geographical standardization was applied to the sub-district mean NDVI values and to the relevant ground yield estimates in order to remove most of the noninteresting information related to variations in land resources. A correlation analysis on the data obtained showed that the best period for yield forecasting was from the end of August to the middle of September. A further improvement in the forecasting capability of the procedure was then achieved by an image-based statistical identification of the most intensively cultivated areas. The final result of the complete methodology was the forecast of crop yield within the middle of September with an acceptable level of accuracy (mean error of 72 kg ha-1).